Introduction to the @cMosaic (cone mosaic) object
Shows basic usage of the new cone mosaic class, @cMosaic.
We first generate an on-axis (zero eccentricity) cMosaic object and compute a number of noisy response instances to a static stimulus with no fixational eye movements, so the computed responses consist of a single time point.
We also illustrate how to see the mosaic with the visualize function and to see the computed cone excitations with the plot function.
At the end we illustrate how get akk the parameters from the cMosaic constructor so you can reliably create the same cMosaic. Or create a second cMosaic with the same parameters and use it to measure a different stimulus.
See Also:
- t_cMosaicPlots
- t_cMosaicGenerate
- t_cMosaicSingleEyeMovementPath
- t_cMosaicMultipleEyeMovementPaths
- t_cMosaicEccDependentAbsorptionEfficacy
- t_cMosaicBenchMark
- t_cMosaicOffAxis
- t_cMosaicFromConeMosaicHex
Generate the ring rays stimulus
ieInit;
scene = sceneCreate('rings rays');
scene = sceneSet(scene, 'fov', 1);
Generate the mosaic
positionDegs = [1 0];
cm = cMosaic(...
'sizeDegs', [1.0 1.0], ... % SIZE: 1.0 degs (x) 0.5 degs (y)
'positionDegs', positionDegs, ... % ECC: (0,0)
'eccVaryingConeBlur', true ...
);
% This is the standard human lens model. It is a good approximation
% over the central 10 deg (see Visual Encoding: Principles and
% Software, in the Circadian Rhythms book, Figure 6).
oi = oiCreate;
%{
% To get an OI specific for this visual field position, use
zCoeffDatabase = 'Artal2012';
eyeside = 'right';
pupilDiamMM = 3.0;
centerpsf = true;
wave = 400:10:700;
ecc = 1; % In degrees
subjectRank = 15; % [10, 40];
oi = ...
oiPosition(zCoeffDatabase, 'position',positionDegs, ...
'pupil diameter', pupilDiamMM, 'subject rank', subjectRank, ...
'wave',wave, ...
'eye side', eyeside,'center psf',centerpsf);
%}
oi = oiCompute(scene, oi);
Visualize of the mosaic and the data
cm.visualize();
To compute a single noise free instance of the excitations
allE = cm.compute(oi);
cm.plot('excitations',allE);
To compute multiple noisy response instances of cone excitation response
instancesNum = 3;
[~, allNoisyE] = cm.compute(oi, 'nTrials', instancesNum);
for ii=1:instancesNum
cm.plot('excitations',allNoisyE(ii,1,:));
end
Make a line regionOfInterest (ROI) and show it superimposed the activations
% For help on ROIs, use this
% regionOfInterest('help');
roiLine = regionOfInterest('shape', 'line', ...
'from', [.5 0.2], 'to', [1.5,-0.2], ...
'thickness', 0.1);
% Show the ROI on top of the activations
cm.plot('roi',allE, 'roi',roiLine);
% Now choose a small circular ROI
roiCircle = regionOfInterest('shape','ellipse',...
'center',[1.3 0],...
'majorAxisDiameter',0.2,...
'minorAxisDiameter',0.2);
cm.plot('roi',allE, 'roi',roiCircle);
Retrieving the excitations within an ROI from all the excitations
roiE = cm.excitations('roi',roiCircle,'all excitations',allE);
mean(roiE)
ans = 92.5677
roiLE = cm.excitations('roi',roiCircle,...
'all excitations',allE, ...
'cone type','L');
mean(roiLE)
ans = 105.5528
% Fewer and lower excitations of S-cones.
[roiSE, roiSIdx] = cm.excitations('roi',roiCircle,...
'all excitations',allE, ...
'cone type','S');
mean(roiSE)
ans = 14.5180
Get the excitations and visualize in one call
[roiE, roiIdx, allE] = cm.excitations('roi',roiCircle,'visualize',true,'all excitations',allE);
Visualize responses - NC code. Thinking what to do
hFig = figure(); clf;
set(hFig, 'Position', [100 300 1500 650]);
activationRange = prctile(allNoisyE(:), [1 99]);
% Noise-free response
ax = subplot(1,2,1);
cm.visualize('figureHandle', hFig, 'axesHandle', ax, ...
'activation', allE, ...
'activationRange', activationRange, ...
'verticalActivationSliceEccentricity', -0.2, ...
'verticalActivationColorBar', true, ...
'plotTitle', 'noise-free response');
% Loop over noisy response instances
ax = subplot(1,2,2);
for k = 1:instancesNum
cm.visualize('figureHandle', hFig, 'axesHandle', ax, ...
'activation', allNoisyE(k,:,:), ...
'activationRange', activationRange, ...
'verticalActivationSliceEccentricity', -0.2, ...
'verticalActivationColorBar', true, ...
'plotTitle', sprintf('noisy response instance (#%d)', k));
pause(1);
end
Making a reproducible cMosaic
% For some calculations you would like to re-generate a repeatable, rather
% than random, cone mosaic. You can use the randomSeed slot for that.
% Here is the first mosaic. The second argument is the list of parameters
% used to create the mosaic.
[cm1, cm1P] = cMosaic(...
'sizeDegs', [1.0 1.0], ...% SIZE: 1.0 degs (x) 0.5 degs (y)
'positionDegs', [1 0], ...% ECC: (0,0)
'eccVaryingConeBlur', true, ...
'randomSeed', 12 ...
);
% Here is the second one, same random seed, so it matches
[cm2, cm2P] = cMosaic(...
'sizeDegs', [1.0 1.0], ...% SIZE: 1.0 degs (x) 0.5 degs (y)
'positionDegs', [1 0], ...% ECC: (0,0)
'eccVaryingConeBlur', true, ...
'randomSeed', 12 ...
);
% The parameters are equal because, well, we sent in the same parameters.
isequal(cm1P,cm2P)
% You can see the two mosaics are the same this way:
cm1.visualize;
cm2.visualize;
To create the mosaic from specifying the parameters, you can do this
% Sometimes you just want to control all the parameters. So using the
% parameter list from above, we can create a mosaic this way.
[cm3, cm3P] = cMosaic(cm1P);
% Check that the parameters remained equal.
isequal(cm1P,cm3P)
% Have a look to see they are equal.
cm3.visualize;
You can change a parameter this way and see that it is different.
cm3P.randomSeed = 11;
cm4 = cMosaic(cm3P);
cm4.visualize;
You can also get the default set of parameters this way
% This creates a random mosaic.
cmP = cMosaicParams;
[cm, cmP1] = cMosaic(cmP);
cm.visualize;
% The random seed is returned. So if you use the parameters again, you
% will get the same mosaic.
cmP1.randomSeed
ans = 849239